Long-Term Asthma Trend Monitoring in New York City: A Mixed Model Approach

Authors

  • Stephen E. Schachterle New York City Department of Health and Mental Hygiene, Queens, NY
  • Robert W. Mathes New York City Department of Health and Mental Hygiene, Queens, NY
  • Marc Paladini New York City Department of Health and Mental Hygiene, Queens, NY
  • Don Weiss New York City Department of Health and Mental Hygiene, Queens, NY

DOI:

https://doi.org/10.5210/ojphi.v5i1.4435

Abstract

The application of syndromic surveillance systems has expanded beyond early event detection to include long-term disease trend monitoring. To address this wider set of priorities, we propose using a general linear mixed model (GLMM) for examining syndrome trends spatially and over time. With the GLMM, we found that New York City asthma rates varied by ZIP code and fluctuated seasonally, but that annual citywide rates did not change from 2007 to 2012. The GLMM estimated rates at multiple spatial and temporal levels, adjusted for clustering with random effects, and integrated covariate demographic data to reduce bias.

Author Biography

Stephen E. Schachterle, New York City Department of Health and Mental Hygiene, Queens, NY

Stephen Schachterle is an analyst with the Syndromic Surveillance Group at the New York City Department of Health and Mental Hygiene.

Downloads

Published

2013-03-23

How to Cite

Schachterle, S. E., Mathes, R. W., Paladini, M., & Weiss, D. (2013). Long-Term Asthma Trend Monitoring in New York City: A Mixed Model Approach. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4435

Issue

Section

Poster Presentations